Unlike other digital signal processing techniques such as the Fast Fourier Transform (FFT) for one-dimensional (1-D) (FFT1) and two-dimensional (2-D) (FFT2) data that assume signal linearity and stationarity, the Hilbert-Huang Transform (HHT) utilizes relationships between an arbitrary signal's local extrema to find a signal instantaneous spectral representation. This can be done in two steps. In the first step, the Huang Empirical Mode Decomposition (EMD) separates an input signal of one variable s(t) into a finite set of narrow-band Intrinsic Mode Functions—{IMF1(t), IMF2(t) . . . IMFk(t)} that add up to signal s(t). Next, the HHT applies the Hilbert Transform to each IMFi(t) signal constituent to obtain the corresponding analytical signal Si(t). From the analytical signal, the HHT generates the Hilbert Spectrum {ω(IMF1(t), ω(IMF2(t) . . . ω(IMFk(t))} at each domain argument t for signal s(t) that was otherwise unobtainable. However, the state-of-the-art HHT Data Processing System (HHT-DPS) works only for 1-D data, as designed, and it is not a real-time system. Accordingly, the development of a reference HHT Data Processing Real-Time System (HHT-DPS-RT) with 2-D capabilities or HHT2 to process large images may be beneficial.